Recognition of Human Identity by Detection of User Activity

The paper describes a system able to recognize the users identity according how she/he looks at the monitor while using a given interface. The system does not need invasive measurements that could limit the naturalness of her/his actions. The proposed approach clusters the sequences of observed points on the screen and characterizes the user identity according the relevant detected patterns. Moreover, the system is able to identify patterns in order to have a more accurate recognition and to create prototypes of natural facial dynamics in user expressions. The possibility to characterize people through facial movements introduces a new perspective on human-machine interaction. For example, a user can obtain different contents according her/his mood or a software interface can modify itself to keep a higher attention from a bored user. The success rate of the classification using only 7 parameters is around 68%. The approach is based on k-means that is tuned to maximize an index involving the number of true-positive detections and conditional probabilities. A different evaluation of this parameter allows to focus on the identification of a single user or to spot a general movement for a wide range of people The experiments show that the performance can reach the 90% of correct recognition.

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